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PyTorch
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Deep learning framework.
- Categories
- Matched as
- pytorch
- torch
Momentum
Each signal is ranked as a percentile among the technologies that have it. A signal without enough history is not counted, and never shown as zero. How momentum works
Momentum
Collecting0 of 5 signalsNo score yet. Momentum is published only when at least 2 of the 5 signals have history. PyTorch has 0, so there is no single number: each signal is shown on its own below. It is not a zero, and nothing is estimated in its place.
Snapshot OCT 24, 2026 · Scoring tech@1
- Official repository stars
- Collecting
- Star growth needs repository snapshots 30 days apart.103,616 stars now
- Member repository stars
- Collecting
- Star growth needs repository snapshots 30 days apart.0 new members in 30 days
- Package adoption
- Not published by its registries
- Its packages’ registries publish no adoption figure, so there is nothing to grow.
- Open models
- No official models
- No official model on Hugging Face, so this signal does not apply.
- News
- Collecting
- News counts once there are 3 events across the last 60 days; it has 2.2 in the last 30 days · 0 in the 30 before
Adoption by registry
The growth of PyTorch’s own packages, one row per registry in that registry’s own unit. Registries count different things over different windows, so they are never added together.
No registry has two observations of PyTorch’s own packages about 30 days apart yet, so there is no growth to show. Nothing is estimated in its place.
News activity
- Events · 7 days
- 2published events that mention it
- Events · 30 days
- 2published events that mention it
- Previous 30 days
- 0events in the 30 days before
Momentum history
Momentum history starts OCT 8, 2026. A chart appears once two daily snapshots carry a score; none does so far.
Repositories
Official 1
- pytorch/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
103.6K stars
Members 794repositories tagged with one of its aliases, most-starred first
- activeloopai/deeplake
Deeplake is AI Data Runtime for Agents. It provides serverless postgres with a multimodal datalake, enabling scalable retrieval and training.
9.2K stars
- NVlabs/Sana
SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformer
9.2K stars
- Cadene/pretrained-models.pytorch
Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.
9.1K stars
- pyro-ppl/pyro
Deep universal probabilistic programming with Python and PyTorch
9.1K stars
- Project-MONAI/MONAI
AI Toolkit for Healthcare Imaging
8.7K stars
- MVIG-SJTU/AlphaPose
Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System
8.6K stars
- netease-youdao/EmotiVoice
EmotiVoice 😊: a Multi-Voice and Prompt-Controlled TTS Engine
8.5K stars
- bitsandbytes-foundation/bitsandbytes
Accessible large language models via k-bit quantization for PyTorch.
8.5K stars
- aladdinpersson/Machine-Learning-Collection
A resource for learning about Machine learning & Deep Learning
8.5K stars
- OptimalScale/LMFlow
An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.
8.5K stars
- XPixelGroup/BasicSR
Open Source Image and Video Restoration Toolbox for Super-resolution, Denoise, Deblurring, etc. Currently, it includes EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, ECBSR, etc. Also support StyleGAN2, DFDNet.
8.4K stars
- utkuozbulak/pytorch-cnn-visualizations
Pytorch implementation of convolutional neural network visualization techniques
8.2K stars
Packages
“Is” packages are PyTorch itself; ecosystem packages depend on them. The two are counted separately and never added together. Adoption is each registry’s own figure, with its percentile within that registry.
Is 1the technology itself, most-adopted first
- torch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Not published by PyPI
Ecosystem 582packages that depend on it, most-adopted first
- composer
Composer is a PyTorch library that enables you to train neural networks faster, at lower cost, and to higher accuracy.
Not published by PyPI - compressai
A PyTorch library and evaluation platform for end-to-end compression research
Not published by PyPI - compressed-tensors
Library for utilization of compressed safetensors of neural network models
Not published by PyPI - concrete-ml
Concrete ML is an open-source set of tools which aims to simplify the use of fully homomorphic encryption (FHE) for data scientists.
Not published by PyPI - concrete-python
A state-of-the-art homomorphic encryption framework
Not published by PyPI - conformer
The convolutional module from the Conformer paper
Not published by PyPI - contextualized-topic-models
Contextualized Topic Models
Not published by PyPI - controlnet-aux
Auxillary models for controlnet
Not published by PyPI - ctgan
Create tabular synthetic data using a conditional GAN
Not published by PyPI - cuequivariance-ops-torch-cu12
cuequivariance-ops-torch - GPU Accelerated Torch Extensions for Equivariant Primitives
Not published by PyPI - cuequivariance-ops-torch-cu13
cuequivariance-ops-torch - GPU Accelerated Torch Extensions for Equivariant Primitives
Not published by PyPI - curated-transformers
A PyTorch library of transformer models and components
Not published by PyPI
Latest news the 3 latest events that mention it
- 18h agoOCT 24, 2026
- 2d agoOCT 22, 2026
- 4mo agoJUL 10, 2026